Adaptive Real-Time Degradation Modeling for Lithium-Ion Batteries in Grid Energy Storage Systems

The increasing integration of renewable energy sources into smart grids has intensified the demand for efficient and reliable energy storage systems (ESSs). Predicting LIB degradation in real time remains a critical challenge, directly affecting the economics and reliability of grid-scale applications. Existing degradation models typically depend on costly and time-consuming laboratory-based cycling tests, limiting their scalability and real-world applicability. This paper proposes a novel Adaptive Real-Time Degradation Model (ARDM) that addresses these limitations by eliminating the need for experimental pre-cycling and instead relying solely on real-time operational data and manufacturer datasheet parameters. The ARDM uniquely and continuously adapts its modeling parameters during live operation, accounting for key stress factors such as depth of discharge, charge/discharge rates, and temperature. Unlike traditional static models, the ARDM is chemistry-agnostic, requiring no prior calibration and achieving degradation prediction errors of less than 0.0005%, representing a substantial improvement over state-of-the-art approaches. Experimental validation on two commercial LIBs under diverse stress conditions confirms the model’s accuracy, adaptability, and robustness. Comparative evaluations demonstrate that ARDM not only outperforms benchmark models but also offers a scalable and cost-effective solution for degradation-aware energy management in grid and electric vehicle applications. This work presents a significant advancement toward practical, real-time LIB health monitoring, empowering energy storage service providers and grid planners with a reliable tool for optimizing battery life, cost-efficiency, and operational decisions.